Automatic food recognition is becoming a solution to perform diet control as it has the ability to release the burden of self diet assessment by offering an easy process that immediately detects the food elements in the picture. This step consisting of accurately segmenting the different areas into the proper food category is crucial to make an accurate calorie estimation. In this thesis, we utilize the PREVENT project as a background to the task of creating a model capable of segmenting food. We decided to carry out the research on a newly annotated dataset FoodSeg103 that consists of a more data-realistic support for the implementation of this study. Most papers performed on FoodSeg103 focus on Vision transformer models that are seen as very trendy but also with computational constraints. We decided to choose DeepLabV3 as a dilation-based semantic segmentation model with main objective of training the model on the dataset and additionally with hope of improving the state-of-the-art results. We set up an iterative optimization process with purpose of maximizing the results and managed to attain 48.27% mIOU (also mentioned as "mIOU all" in the thesis). We also obtained a significant difference in average mIOU troughout all random search experiments in comparison to bayesian optimization experiments.This study has not overpassed the state-of-the-art performance but has managed to settle 1% behind, BEIT v2 Large remaining in first position with 49.4% mIOU.